Factor and figure management
Loading libraries
Part 1: Factor management
- Drop Oceania:
- Reorder the levels of the country or continent
Part 2: File I/O
- Reading and writing data set into a file
Part 3: Visualization design
Part 4: Writing figures to file
But I want to do more!

Factor and figure management

Loading libraries

Let us load the necessary library required for this homework.

suppressPackageStartupMessages(library(tidyverse))

## Warning: replacing previous import by 'tibble::as_tibble' when loading
## 'broom'

## Warning: replacing previous import by 'tibble::tibble' when loading 'broom'

suppressPackageStartupMessages(library(gapminder))
suppressPackageStartupMessages(library(gridExtra))

Part 1: Factor management

We shall be using the gapminder data set for this exercise.

Drop Oceania:

We begin by removing the rows for the continent Oceania from the gapminder data set. Before we do this, let us use the str() function to check the number of factors in the gapminder data set and the number of level each factor has.

gapminder %>% #loads the gapminder data set and pipes it into the function in the next line
  str()

## Classes 'tbl_df', 'tbl' and 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

This ouput shows that the gapminder data set has two factors; country and continent. Country has 142 levels, while continent has 5 levels. Also we see that this data set has 1704 rows and 6 columns. We can visualized this data using a barchart as shown below;

gapminder %>%  # loads the gapminder data and pipes it into the next line
  ggplot(aes(continent)) + geom_bar() + # use ggplot to produce a barhcart
  ggtitle("The number of observations for each continent")

Let us arrange this data according to the population in ascending and plot a barchart of the new data set. We want to see if the arrange() function has an effect on the plot.

ArrGap <- gapminder %>%  # loads the gapminder data and pipes it into the next line
            arrange(pop) # arranges the data according to the population in ascending order
 
# display few rows of data
ArrGap %>%  # loads the ArrGap data and pipes it into the next line
  head() %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Sao Tome and Principe	Africa	1952	46.471	60011	879.5836
Sao Tome and Principe	Africa	1957	48.945	61325	860.7369
Djibouti	Africa	1952	34.812	63149	2669.5295
Sao Tome and Principe	Africa	1962	51.893	65345	1071.5511
Sao Tome and Principe	Africa	1967	54.425	70787	1384.8406
Djibouti	Africa	1957	37.328	71851	2864.9691

# plotting the data
 ArrGap %>% 
  ggplot(aes(continent)) + geom_bar() + # use ggplot to produce a barhcart 
  ggtitle("The number of observations for each continent")

Observe from the table and figures above that the arrange() function re-arranges the rows of the data but this re-arrangement does not affect the figure produced after arranging the data.

Now, let us extract out data from the gapminder data set.

# create a subset of gapminder data that does not contain Oceania, call this data set 'GapNoOcean'
GapNoOcean <- gapminder %>% #loads the data set and pipes it into the function in the next line
        filter(continent != "Oceania")


# displaying table nicely
head(GapNoOcean,15) %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Afghanistan	Asia	1952	28.801	8425333	779.4453
Afghanistan	Asia	1957	30.332	9240934	820.8530
Afghanistan	Asia	1962	31.997	10267083	853.1007
Afghanistan	Asia	1967	34.020	11537966	836.1971
Afghanistan	Asia	1972	36.088	13079460	739.9811
Afghanistan	Asia	1977	38.438	14880372	786.1134
Afghanistan	Asia	1982	39.854	12881816	978.0114
Afghanistan	Asia	1987	40.822	13867957	852.3959
Afghanistan	Asia	1992	41.674	16317921	649.3414
Afghanistan	Asia	1997	41.763	22227415	635.3414
Afghanistan	Asia	2002	42.129	25268405	726.7341
Afghanistan	Asia	2007	43.828	31889923	974.5803
Albania	Europe	1952	55.230	1282697	1601.0561
Albania	Europe	1957	59.280	1476505	1942.2842
Albania	Europe	1962	64.820	1728137	2312.8890

Let us use the str() function to check the factors and their levels of our new data set.

GapNoOcean %>% #loads the data set and pipes it into the function in the next line
  str()

## Classes 'tbl_df', 'tbl' and 'data.frame':    1680 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

After removing the rows for the countries in Oceania from the gapminder data set, the new data set has 1680 rows, although, the number of factors in the new data frame still remains 5. Let us check the levels in the continent factor again using the level() function.

levels(GapNoOcean$continent)  # displays the number of levelsof the continent factor

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

This also shows that there are 5 level, it shows both the used and unused levels.

GapNoOcean %>% #loads the data set and pipes it into the function in the next line
  ggplot(aes(continent)) + geom_bar() +  # plots a barchart of the data
  scale_x_discrete(drop=FALSE) + ggtitle("The number of observations for each continent")

This plot shows that the continent Oceania is still a level in the data, althogh it has no entries. Let us arrange this new data set according to the number of observations in ascending order.

GapNoOcean %>%  #loads the data set and pipes it into the function in the next line
  arrange(pop) %>%  # arranges the data according to the population size in ascending order
  ggplot(aes(continent)) + geom_bar()  + # plots barchart
  ggtitle("The number of observations for each continent")

After arranging re-arranging the data, the Oceania continent is dropped from the barchart.

Let us remove unused levels in the continent.

GapNoOceanD <- GapNoOcean %>%
                droplevels() # drops the unused levels

str(GapNoOceanD) # displays the structure of the data

## Classes 'tbl_df', 'tbl' and 'data.frame':    1680 obs. of  6 variables:
##  $ country  : Factor w/ 140 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 4 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

levels(GapNoOceanD$continent) # shows the levels in the continent factor

## [1] "Africa"   "Americas" "Asia"     "Europe"

It now shows that the continent factor has only 4 levels.

There are other ways we can drop unused levels, let us check them out!

forcats::fct_drop(GapNoOcean$continent) %>% # drops unused levels
  levels() # shows the levels in the continent factor

## [1] "Africa"   "Americas" "Asia"     "Europe"

fct_drop(GapNoOcean$continent) %>%  # drops unused levels
  levels()  # shows the levels in the continent factor

## [1] "Africa"   "Americas" "Asia"     "Europe"

Reorder the levels of the country or continent

Let us start by plotting a barchart of the GapNoOceanD data.

GapNoOceanD %>%  # loads the data set and pipes it into the next line
  ggplot(aes(continent)) + geom_bar() +  # plots the barchart
  scale_x_discrete(drop=FALSE)  + ggtitle("The number of observations for each continent")

The continent factor can be reordered in descending order of freqency on the barchart;

GapNoOceanD$continent %>% # loads the data set and pipes it into the next line
  fct_infreq() %>% # reorders level in descending order of frequency
   qplot()  + ggtitle("The number of observations for each continent in descending order") # plots barchart

This ordering is reversed using the forcats::fct_rev() function.

GapNoOceanD$continent %>% # loads the data set and pipes it into the next line
  forcats::fct_infreq() %>%  # reorders level in descending order of frequency
  forcats::fct_rev() %>%  # reverses the previous order
   qplot()  + ggtitle("The number of observations for each continent after reversing level")

Let us present a boxplot of the reordering of continents based on some principled summary of pop.

GapNoOceanD %>%
  ggplot( aes(continent,pop)) +   geom_boxplot() +
  scale_y_log10()  + ggtitle("Boxplot of population for each continent ordered by the median population")

GapNoOceanD %>%  
  group_by(continent) %>%
  mutate(median = median((pop))) %>%
  arrange(median) %>%
  ggplot( aes(continent,pop)) +   geom_boxplot(fill='pink') +
  scale_y_log10()  + ggtitle("Boxplot of population for each continent ordered by the median population")

GapNoOceanD %>%
  mutate(continent= fct_reorder(continent,pop)) %>%
  ggplot( aes(continent,pop)) +   geom_boxplot(fill='pink') +
  scale_y_log10()  + ggtitle("Boxplot of population for each continent ordered by the median population")

Reorder by median population of each continent

GapNoOceanD %>% # loads data and pipes it to the next line
  mutate(continent= fct_reorder(continent,pop)) %>%   # reorders the data
  ggplot( aes(continent,pop)) +   geom_boxplot() + # plots a boxplot
  scale_y_log10()  + ggtitle("Population of each continent reordered by median population")

Reorder by mean population of each continent

GapNoOceanD %>% # loads data and pipes it to the next line
  mutate(continent= fct_reorder(continent,pop, mean)) %>%  # reorders the data
  ggplot( aes(continent, pop)) +   geom_boxplot() +  # plots a boxplot
  scale_y_log10()  + ggtitle("Population of each continent reordered by mean population")

Reorder by maximum population of each continent

GapNoOceanD %>% # loads data and pipes it to the next line
  mutate(continent= fct_reorder(continent,pop, max)) %>%  # reorders the data
  ggplot( aes(continent, pop)) +   geom_boxplot() +  # plots a boxplot
  scale_y_log10()  + ggtitle("Population of each continent reordered by maximum population")

Reorder by minimum population of each continent

GapNoOceanD %>% # loads data and pipes it to the next line
  mutate(continent= fct_reorder(continent,pop, min )) %>%   # reorders the data
  ggplot( aes(continent,pop)) +   geom_boxplot() +  # plots a boxplot
  scale_y_log10() + ggtitle("Population of each continent reordered by minimum population")

Part 2: File I/O

Reading and writing data set into a file

First, let us extract the data set to be used for this exercise from the gapminder data set.

NewGap <- gapminder %>%  # loads data and pipes it to the next line
            filter(continent == "Europe"|continent == "Asia"|continent == "Africa"|continent == "Oceania") %>%
            arrange(gdpPercap) 

str(NewGap) # displays structure of new data set

## Classes 'tbl_df', 'tbl' and 'data.frame':    1404 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 28 28 74 53 28 42 88 74 18 42 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 3 1 1 1 ...
##  $ year     : int  2002 2007 1952 1952 1997 1952 1952 1957 1952 1957 ...
##  $ lifeExp  : num  45 46.5 42.1 32.5 42.6 ...
##  $ pop      : int  55379852 64606759 748747 580653 47798986 1438760 20092996 813338 2445618 1542611 ...
##  $ gdpPercap: num  241 278 299 300 312 ...

# display few rows of new data set
NewGap %>% # loads data and pipes it to the next line
    head() %>%
    knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.1659
Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.5519
Lesotho	Africa	1952	42.138	748747	298.8462
Guinea-Bissau	Africa	1952	32.500	580653	299.8503
Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.1884
Eritrea	Africa	1952	35.928	1438760	328.9406

Let us create a boxplot of the extracted data and name it Default.

Default <-NewGap %>% # loads data and pipes it to the next line
            ggplot( aes(continent,gdpPercap)) +   geom_boxplot(fill='green') + # plots a boxplot
            scale_y_log10()  + ggtitle("Without factor re-order") # scales the y-axis and put title on the graph

Default

Now, we reorder the factor levels and create another boxplot.

NewGapReorder <-NewGap %>%  # loads data and pipes it to the next line
  mutate(continent= fct_reorder(continent,gdpPercap,max))   # reorders the level of the continent using the max population

P <- NewGapReorder %>%  # loads data and pipes it to the next line
  ggplot( aes(continent,gdpPercap)) +   geom_boxplot(fill='pink') + # produces a boxplot
  scale_y_log10()  + ggtitle("Re-ordering factor by max. GdpPercap")

# put the two boxplots side-by-side
grid.arrange(Default,P,ncol=2,top="Comparing the Boxplot of GdpPerCap for the continents with and without factor re-order")

Next, we display the ordering of the levels of continent.

levels(NewGapReorder$continent)  # displays the levels of the selected factor the way the are ordered

## [1] "Africa"   "Oceania"  "Europe"   "Asia"     "Americas"

Let us create another plot of the reorder data and assign it to the variable PP. This plot will be places side-by-side to the new plot generated from the data read from files in order to determine which of the writing and reading format preserves ordering.

GapOrdered <-NewGapReorder %>%  # loads data and pipes it to the next line
  mutate(continent= fct_reorder(continent,gdpPercap,max)) %>%  # reorder continent by max population
  ggplot( aes(continent,gdpPercap)) +   geom_boxplot(fill='pink') +  # produces boxplot
  scale_y_log10()  + ggtitle("Data written to file")

`read_csv()` and `write_csv()`

Writing the reordered data to file.

write_csv(NewGapReorder,"NewGap") # writes the gapminder data set into a file called 'MyGapMinder', in .csv format

Reading back from file.

# reading data from file
GapCSV <- read_csv("NewGap")  # reads the gapminder file in csv format

## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_integer(),
##   gdpPercap = col_double()
## )

# displays the structure
GapCSV %>%
  str()

## Classes 'tbl_df', 'tbl' and 'data.frame':    1404 obs. of  6 variables:
##  $ country  : chr  "Congo, Dem. Rep." "Congo, Dem. Rep." "Lesotho" "Guinea-Bissau" ...
##  $ continent: chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ year     : int  2002 2007 1952 1952 1997 1952 1952 1957 1952 1957 ...
##  $ lifeExp  : num  45 46.5 42.1 32.5 42.6 ...
##  $ pop      : int  55379852 64606759 748747 580653 47798986 1438760 20092996 813338 2445618 1542611 ...
##  $ gdpPercap: num  241 278 299 300 312 ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 6
##   .. ..$ country  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ continent: list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ year     : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ lifeExp  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ pop      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ gdpPercap: list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

# display in table
GapCSV %>%
  head() %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.1659
Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.5519
Lesotho	Africa	1952	42.138	748747	298.8462
Guinea-Bissau	Africa	1952	32.500	580653	299.8503
Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.1884
Eritrea	Africa	1952	35.928	1438760	328.9406

Observe that the structure is not the same as the data written to file, the factors continent and country are now character, although the table is still the same.

P2 <- GapCSV %>%
  ggplot( aes(continent,gdpPercap)) +  geom_boxplot() +
  scale_y_log10() + ggtitle("Data read from file")

grid.arrange(GapOrdered,P2,ncol=2,top="Comparing the boxplots otained from data written to file and the one from data read from file")

`read_delim()` and `write_delim()`

Writing to file:

write_delim(NewGapReorder,"NewGap.txt",delim = "$")  # writes the data to file

Reading from file:

GapTxt <- read_delim("NewGap.txt", delim="$")  # reads data from file

## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_integer(),
##   gdpPercap = col_double()
## )

str(GapTxt) # display the structure of the data

## Classes 'tbl_df', 'tbl' and 'data.frame':    1404 obs. of  6 variables:
##  $ country  : chr  "Congo, Dem. Rep." "Congo, Dem. Rep." "Lesotho" "Guinea-Bissau" ...
##  $ continent: chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ year     : int  2002 2007 1952 1952 1997 1952 1952 1957 1952 1957 ...
##  $ lifeExp  : num  45 46.5 42.1 32.5 42.6 ...
##  $ pop      : int  55379852 64606759 748747 580653 47798986 1438760 20092996 813338 2445618 1542611 ...
##  $ gdpPercap: num  241 278 299 300 312 ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 6
##   .. ..$ country  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ continent: list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ year     : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ lifeExp  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ pop      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ gdpPercap: list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

# display few row in table
GapTxt %>%
  head() %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.1659
Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.5519
Lesotho	Africa	1952	42.138	748747	298.8462
Guinea-Bissau	Africa	1952	32.500	580653	299.8503
Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.1884
Eritrea	Africa	1952	35.928	1438760	328.9406

Similar to .csv format, the structure of the data read from file is not the same as the data written to file, the factors continent and country are now character, although the table is still the same. We can also verify this from the plots.

P3 <- GapTxt %>%
 ggplot( aes(continent,gdpPercap)) +  geom_boxplot() +
  scale_y_log10() + ggtitle("Data read from file")

grid.arrange(GapOrdered,P3,ncol=2,top="Comparing the boxplots otained from data written to file and the one from data read from file")

In addition, we can write data into a file in such a way that each of the columns are separated by tab. This is the TSV format, meaning tab separated format

`read_delim()` and `write_delim()` separated by tab

Writing data to file:

write_delim(NewGapReorder,"NewGap.tsv",delim = "\t")  # writes the data to file

Reading data from file:

GapTab <- read_delim("NewGap.tsv", delim="\t")  # reads the gapminder file in csv format

## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_integer(),
##   gdpPercap = col_double()
## )

str(GapTab) # display structure of data

## Classes 'tbl_df', 'tbl' and 'data.frame':    1404 obs. of  6 variables:
##  $ country  : chr  "Congo, Dem. Rep." "Congo, Dem. Rep." "Lesotho" "Guinea-Bissau" ...
##  $ continent: chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ year     : int  2002 2007 1952 1952 1997 1952 1952 1957 1952 1957 ...
##  $ lifeExp  : num  45 46.5 42.1 32.5 42.6 ...
##  $ pop      : int  55379852 64606759 748747 580653 47798986 1438760 20092996 813338 2445618 1542611 ...
##  $ gdpPercap: num  241 278 299 300 312 ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 6
##   .. ..$ country  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ continent: list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ year     : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ lifeExp  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ pop      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ gdpPercap: list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

# display few rows of data in table
GapTab %>%
  head() %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.1659
Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.5519
Lesotho	Africa	1952	42.138	748747	298.8462
Guinea-Bissau	Africa	1952	32.500	580653	299.8503
Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.1884
Eritrea	Africa	1952	35.928	1438760	328.9406

Here also, the structure of the data read from file is not the same as the data written to file, the factors continent and country are now character, although the table is still the same. Let display a boxplot of the data side-by-side.

P4 <- GapTab %>%
 ggplot( aes(continent,gdpPercap)) +  geom_boxplot() +
  scale_y_log10() + ggtitle("Data read from file")

grid.arrange(GapOrdered,P4,ncol=2,top="Comparing the boxplots otained from data written to file and the one from data read from file")

`readRDS` and `saveRDS()`

Writing to file:

saveRDS(NewGapReorder,"NewGap.rds")  # writes the data set in compressed format (.rds format)

This functions writes data to file in a compressed format.

GapRDS <- readRDS("NewGap.rds")  # reads from file

str(GapRDS) # display the structure

## Classes 'tbl_df', 'tbl' and 'data.frame':    1404 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 28 28 74 53 28 42 88 74 18 42 ...
##  $ continent: Factor w/ 5 levels "Africa","Oceania",..: 1 1 1 1 1 1 4 1 1 1 ...
##  $ year     : int  2002 2007 1952 1952 1997 1952 1952 1957 1952 1957 ...
##  $ lifeExp  : num  45 46.5 42.1 32.5 42.6 ...
##  $ pop      : int  55379852 64606759 748747 580653 47798986 1438760 20092996 813338 2445618 1542611 ...
##  $ gdpPercap: num  241 278 299 300 312 ...

# display few rows of data in table
GapRDS %>%
  head() %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.1659
Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.5519
Lesotho	Africa	1952	42.138	748747	298.8462
Guinea-Bissau	Africa	1952	32.500	580653	299.8503
Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.1884
Eritrea	Africa	1952	35.928	1438760	328.9406

Unlike, the previous format we have seen, this format preserves the structure of the data when reading from file. Let us confirm that the level are still in the same order as it was written to the file.

levels(GapRDS$continent) # displays the levels of the selected factor in proper order

## [1] "Africa"   "Oceania"  "Europe"   "Asia"     "Americas"

Observe that the ordering is also preserved. Yay! Let us check the boxplots:

P5 <- GapRDS %>%
 ggplot( aes(continent,gdpPercap)) +  geom_boxplot() +
  scale_y_log10() + ggtitle("Data read from file")


grid.arrange(GapOrdered,P5,ncol=2,top="Comparing the boxplots otained from data written to file and the one from data read from file")

The ordering is also preserved in the figures.

`dput()` and `dget()` functions

Writing to file:

dput(NewGapReorder,"MyGapMinderDput.txt")  # writes data to file

Reading from file:

GapDput <- dget("MyGapMinderDput.txt")  # reads data from file

str(GapDput) # displays structure

## Classes 'tbl_df', 'tbl' and 'data.frame':    1404 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 28 28 74 53 28 42 88 74 18 42 ...
##  $ continent: Factor w/ 5 levels "Africa","Oceania",..: 1 1 1 1 1 1 4 1 1 1 ...
##  $ year     : int  2002 2007 1952 1952 1997 1952 1952 1957 1952 1957 ...
##  $ lifeExp  : num  45 46.5 42.1 32.5 42.6 ...
##  $ pop      : int  55379852 64606759 748747 580653 47798986 1438760 20092996 813338 2445618 1542611 ...
##  $ gdpPercap: num  241 278 299 300 312 ...

# display few rows in table
GapDput %>%
  head() %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap
Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.1659
Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.5519
Lesotho	Africa	1952	42.138	748747	298.8462
Guinea-Bissau	Africa	1952	32.500	580653	299.8503
Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.1884
Eritrea	Africa	1952	35.928	1438760	328.9406

The structure of the data is also preserved with this format. How about we plot a boxplot of the data read from file and compare with the boxplot of the data written to file?

P6 <- GapDput %>%
 ggplot( aes(continent,gdpPercap)) +  geom_boxplot() +
  scale_y_log10() + ggtitle("Data read from file")

grid.arrange(GapOrdered,P6,ncol=2,top="Comparing the boxplots otained from data written to file and the one from data read from file")

These figures are identical, which confirms that the structure of the data is preserved.

Part 3: Visualization design

We begin by loading the necessary libraries required for this exercise.

suppressPackageStartupMessages(library(scales))
suppressPackageStartupMessages(library(plotly))

FirstAttempt <- gapminder %>%
            ggplot(aes(pop,lifeExp)) + geom_point(aes(color=gdpPercap), alpha = 0.3) 

Attempt1 <- FirstAttempt  +  scale_x_log10() + ggtitle("Attempt 1")
Attempt1

Attempt2 <-  FirstAttempt +  # loads the previous plot
            scale_color_continuous(trans="log10",       # makes the colorign continuous
                                   breaks = 5^(1:10),             # labels the legend in multiples of 5
                                   labels = comma_format()        # wtites the legend label in comma separated format
                                    ) + 
            scale_y_continuous(breaks = 10*(1:10)) + # labels the y-axis in multiples of 10
            scale_x_continuous(trans = "log10",   # puts the x-axis in log10 scale
                               labels = comma_format(),  # put the x-label in comma format
                               ) + ggtitle("Attempt 2: scale_color_continuous")
Attempt2

Attempt3 <-  FirstAttempt +  # loads the previous plot
            scale_color_distiller(trans="log10",       # makes the colorign continuous
                                   breaks = 5^(1:10),             # labels the legend in multiples of 5
                                   labels = comma_format(),        # wtites the legend label in comma separated format
                                    palette = "Reds") + 
            scale_y_continuous(breaks = 10*(1:10)) + # labels the y-axis in multiples of 10
            scale_x_continuous(trans = "log10",   # puts the x-axis in log10 scale
                               labels = comma_format(),  # put the x-label in comma format
                               )  + ggtitle("Attempt 3: scale_color_distiller")
                  
Attempt3

Attempt4 <-  FirstAttempt +  # loads the previous plot
            scale_color_viridis_c(trans="log10",       # makes the colorign continuous
                                   breaks = 5^(1:10),             # labels the legend in multiples of 5
                                   labels = comma_format()        # wtites the legend label in comma separated format
                                   ) + 
            scale_y_continuous(breaks = 10*(1:10)) + # labels the y-axis in multiples of 10
            scale_x_continuous(trans = "log10",   # puts the x-axis in log10 scale
                               labels = comma_format(),  # put the x-label in comma format
                               )   + ggtitle("Attempt 4: scale_color_viridis_c")
                  
Attempt4

Putting all the attmepts together, we have

 grid.arrange(Attempt1,Attempt2,Attempt3,Attempt4,ncol=2,top="Different Attempts")

Now let us convert each of our attempts to a plotly

plotly1 <- ggplotly(Attempt1)

## Warning: replacing previous import by 'Rcpp::evalCpp' when loading 'later'

## Warning: replacing previous import by 'shiny::validateCssUnit' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::br' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::tags' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::div' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::h1' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::h2' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::h3' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::h4' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::h5' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::h6' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::knit_print.html' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::tagSetChildren' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::includeScript' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::em' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::tagAppendChild' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::is.singleton' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::includeHTML' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::includeMarkdown' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::code' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::tagList' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::a' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::tagAppendAttributes' when
## loading 'crosstalk'

## Warning: replacing previous import by 'shiny::singleton' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::hr' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::p' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::suppressDependencies' when
## loading 'crosstalk'

## Warning: replacing previous import by 'shiny::tagAppendChildren' when
## loading 'crosstalk'

## Warning: replacing previous import by 'shiny::includeText' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::pre' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::span' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::withTags' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::htmlTemplate' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::img' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::tag' when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::includeCSS' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::knit_print.shiny.tag' when
## loading 'crosstalk'

## Warning: replacing previous import by 'shiny::knit_print.shiny.tag.list'
## when loading 'crosstalk'

## Warning: replacing previous import by 'shiny::strong' when loading
## 'crosstalk'

## Warning: replacing previous import by 'shiny::HTML' when loading
## 'crosstalk'

plotly1

plotly4 <- ggplotly(Attempt4)
plotly4

We observe from these figures that the plotly() allows for more interaction with the figures, for example, we can zoom in and out, select part of the figure, save figure as .png file, plus many other interactiveness. It evens shows the value of the variables in the plot when the mouse is placed over each point in the graph.

Let do some activities with the theme() function. First, we plot some figures form the gapminder data, after which we make it more presentable.

lifeExp_vs_Gdp <- gapminder %>%
    ggplot(aes(gdpPercap, lifeExp)) + geom_point(aes(color=continent)) + 
    scale_x_log10() + 
    facet_wrap(~continent) +
    ggtitle( "Life Expectancy vs Gdp per capital for different continents")

lifeExp_vs_Gdp

lifeExp_vs_Gdp +
    theme_bw() +
    theme(axis.text = element_text(size=16),
          strip.text = element_text(size=16,color = "blue"),
          strip.background = element_rect(fill = "orange"),
          panel.background = element_rect(fill = "pink"))

Plotly

Let us plot directly using the ‘plot_ly()’ function

gapminder %>%
  plot_ly(x = ~gdpPercap, 
        y = ~pop, 
        type = "scatter",
        colors = 'red',
        mode = "markers",
        marker=list(color="green" , size=5 , opacity=1.5) ,
        opacity = 0.5) %>% 
    layout(xaxis = list(type = "log"),yaxis = list(type = "log"))

gapminder %>%
  plot_ly(x = ~lifeExp, 
        y = ~gdpPercap, 
        type = "scatter",
        colors = 'red',
        mode = "markers",
        marker=list(color="red" , size=10 , opacity=0.5) ,
        opacity = 0.5) %>% 
    layout(xaxis = list(type = "log"),yaxis = list(type = "log"))

Part 4: Writing figures to file

NewGap_Boxplot <-NewGap %>%
  mutate(continent= fct_reorder(continent,gdpPercap,max)) %>%
  arrange(gdpPercap) %>%
  ggplot( aes(continent,gdpPercap)) +   geom_boxplot(fill='pink') +
  scale_y_log10()  + ggtitle("Boxplot of population for each continent ordered by the median population")


ggsave("Boxplot.png", NewGap_Boxplot)

## Saving 7 x 5 in image

graph

ggsave("Boxplot2.png", plot = NewGap_Boxplot, width = 2, height = 2)

graph

ggsave("Boxplot3.png", plot = NewGap_Boxplot, width = 5, height = 2, limitsize =TRUE, dpi = 300)

graph

ggsave("Boxplot4.pdf", plot = NewGap_Boxplot, width = 5, height = 2, limitsize =TRUE, dpi = 300)

graph

But I want to do more!

Gapminder version

Let us extract the rows for some countries in the gapminder data:

Sport_Gap <- gapminder %>%
    filter(year == "1952") %>%
    filter(country %in% c("Nigeria", "Canada", "India", "Sweden", "Brazil")) %>%
    mutate(Sports = factor(c("Soccer","Ice hockey","Cricket","Biking","Baseball"))) %>%
    droplevels()

Sport_Gap %>%
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap	Sports
Brazil	Americas	1952	50.917	56602560	2108.9444	Soccer
Canada	Americas	1952	68.750	14785584	11367.1611	Ice hockey
India	Asia	1952	37.373	372000000	546.5657	Cricket
Nigeria	Africa	1952	36.324	33119096	1077.2819	Biking
Sweden	Europe	1952	71.860	7124673	8527.8447	Baseball

Sport_Gap %>%
  str

## Classes 'tbl_df', 'tbl' and 'data.frame':    5 obs. of  7 variables:
##  $ country  : Factor w/ 5 levels "Brazil","Canada",..: 1 2 3 4 5
##  $ continent: Factor w/ 4 levels "Africa","Americas",..: 2 2 3 1 4
##  $ year     : int  1952 1952 1952 1952 1952
##  $ lifeExp  : num  50.9 68.8 37.4 36.3 71.9
##  $ pop      : int  56602560 14785584 372000000 33119096 7124673
##  $ gdpPercap: num  2109 11367 547 1077 8528
##  $ Sports   : Factor w/ 5 levels "Baseball","Biking",..: 5 4 3 2 1

Homework 5

Sarafa Iyaniwura

October 12, 2018

Factor and figure management

Loading libraries

Part 1: Factor management

Drop Oceania:

Reorder the levels of the country or continent

Part 2: File I/O

Reading and writing data set into a file

`read_csv()` and `write_csv()`

`read_delim()` and `write_delim()`

`read_delim()` and `write_delim()` separated by tab

`readRDS` and `saveRDS()`

`dput()` and `dget()` functions

Part 3: Visualization design

Part 4: Writing figures to file

But I want to do more!

Homework 5

Sarafa Iyaniwura

October 12, 2018

Factor and figure management

Loading libraries

Part 1: Factor management

Drop Oceania:

Reorder the levels of the country or continent

Part 2: File I/O

Reading and writing data set into a file

read_csv() and write_csv()

read_delim() and write_delim()

read_delim() and write_delim() separated by tab

readRDS and saveRDS()

dput() and dget() functions

Part 3: Visualization design

Part 4: Writing figures to file

But I want to do more!

`read_csv()` and `write_csv()`

`read_delim()` and `write_delim()`

`read_delim()` and `write_delim()` separated by tab

`readRDS` and `saveRDS()`

`dput()` and `dget()` functions